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Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients.

ABSTRACT: Functional status (FS) before intensive care unit (ICU) admission is associated with short-term and long-term outcomes among critically ill patients. However, measures of FS are generally not integrated into ICU-specific mortality prediction models.This retrospective cohort study used prospectively collected data from 9638 consecutive patients admitted to a single ICU between 1 October 2005 and 30 September 2015. For each ICU admission, FS was prospectively determined and classified into three discrete categories based on performance of basic daily living activities (FS1 - fully independent; FS2 - partly dependent; FS3 - completely dependent). We prospectively calculated Acute Physiology and Chronic Health Evaluation (APACHE) IV predicted mortality percentage (APIV PM) for each admission and calculated observed-expected mortality ratios (OEMR), stratified by FS category and APIV PM. We calculated area under the receiver operator characteristic curve (AUC) for APIV PM and mortality for the entire cohort and the three FS categories.Patients had a median (IQR) age of 67 (52-80) years and mean (SD) APIV PM was 18.3% (24.3%). Of these, 7714 (80.0%) were classified as FS1, 1728 (17.9%) as FS2 and 196 (2.0%) as FS3. FS1 patients were younger, had less comorbid disease, and lower APIV PM compared to FS2 and FS3. The OEMR were significantly lower for FS1 (0.67) than FS2 (0.93) or FS3 (0.90) (p?

SUBMITTER: Krinsley JS

PROVIDER: S-EPMC5433010 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients.

Krinsley James S JS Wasser Thomas T Kang Gina G Bagshaw Sean M SM

Critical care (London, England) 20170515 1

<h4>Background</h4>Functional status (FS) before intensive care unit (ICU) admission is associated with short-term and long-term outcomes among critically ill patients. However, measures of FS are generally not integrated into ICU-specific mortality prediction models.<h4>Methods</h4>This retrospective cohort study used prospectively collected data from 9638 consecutive patients admitted to a single ICU between 1 October 2005 and 30 September 2015. For each ICU admission, FS was prospectively det ...[more]

PMID: 28506290

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Project description:RationaleCognitive impairment is common among older adults, yet little is known about the association of pre-intensive care unit cognitive status with outcomes relevant to older adults maintaining independence after a critical illness.ObjectivesTo evaluate whether pre-intensive care unit cognitive status is associated with post-intensive care unit disability, new nursing home admission, and mortality after a critical illness among older adults.MethodsIn this prospective cohort study, 754 persons aged 70 years or more were monitored from March 1998 to December 2013 with monthly assessments of disability. Cognitive status was assessed every 18 months, using the Mini-Mental State Examination (range, 0-30), with scores classified as 28 or higher (cognitively intact), 24-27 (minimal impairment), and less than 24 (moderate impairment). The primary outcome was disability count (range, 0-13), assessed monthly over 6 months after an intensive care unit stay. The secondary outcomes were incident nursing home admission and time to death after intensive care unit admission. The analytic sample included 391 intensive care unit admissions.ResultsThe mean age was 83.5 years. The prevalence of moderate impairment, minimal impairment, and intact cognition (the comparison group) was 17.3, 46.2, and 36.5%, respectively. In the multivariable analysis, moderate impairment was associated with nearly a 20% increase in disability over the 6-month follow-up period (adjusted relative risk, 1.19; 95% confidence interval, 1.04-1.36), and minimal impairment was associated with a 16% increase in post-intensive care unit disability (adjusted relative risk, 1.16; 95% confidence interval, 1.02-1.32). Moderate impairment was associated with more than double the likelihood of a new nursing home admission (adjusted odds ratio, 2.37; 95% confidence interval, 1.01-5.55). Survival differed significantly across the three cognitive groups (log-rank P = 0.002), but neither moderate impairment (adjusted hazard ratio, 1.19; 95% confidence interval, 0.65-2.19) nor minimal impairment (adjusted hazard ratio, 1.00; 95% confidence interval, 0.61-1.62) was significantly associated with mortality in the multivariable analysis.ConclusionsAmong older adults, any impairment (even minimal) in pre-intensive care unit cognitive status was associated with an increase in post-intensive care unit disability over the 6 months after a critical illness; moderate cognitive impairment doubled the likelihood of a new nursing home admission. Pre-intensive care unit cognitive impairment was not associated with mortality from intensive care unit admission through 6 months of follow-up. Pre-intensive care unit cognitive status may provide prognostic information about the likelihood of older adults maintaining independence after a critical illness.

Project description:BackgroundRisk prediction models allow clinicians to forecast which individuals are at a higher risk for developing a particular outcome. We developed and internally validated a delirium prediction model for incident delirium parameterized to patient ICU admission acuity.MethodsThis retrospective, observational, fourteen medical-surgical ICU cohort study evaluated consecutive delirium-free adults surviving hospital stay with ICU length of stay (LOS) greater than or equal to 24 hours with both an admission APACHE II score and an admission type (e.g., elective post-surgery, emergency post-surgery, non-surgical) in whom delirium was assessed using the Intensive Care Delirium Screening Checklist (ICDSC). Risk factors included in the model were readily available in electric medical records. Least absolute shrinkage and selection operator logistic (LASSO) regression was used for model development. Discrimination was determined using area under the receiver operating characteristic curve (AUC). Internal validation was performed by cross-validation. Predictive performance was determined using measures of accuracy and clinical utility was assessed by decision-curve analysis.ResultsA total of 8,878 patients were included. Delirium incidence was 49.9% (n = 4,431). The delirium prediction model was parameterized to seven patient cohorts, admission type (3 cohorts) or mean quartile APACHE II score (4 cohorts). All parameterized cohort models were well calibrated. The AUC ranged from 0.67 to 0.78 (95% confidence intervals [CI] ranged from 0.63 to 0.79). Model accuracy varied across admission types; sensitivity ranged from 53.2% to 63.9% while specificity ranged from 69.0% to 74.6%. Across mean quartile APACHE II scores, sensitivity ranged from 58.2% to 59.7% while specificity ranged from 70.1% to 73.6%. The clinical utility of the parameterized cohort prediction model to predict and prevent incident delirium was greater than preventing incident delirium by treating all or none of the patients.ConclusionsOur results support external validation of a prediction model parameterized to patient ICU admission acuity to predict a patients' risk for ICU delirium. Classification of patients' risk for ICU delirium by admission acuity may allow for efficient initiation of prevention measures based on individual risk profiles.

Project description:BackgroundIt has not been verified whether there is a correlation between admission hyperlactatemia and outcomes in critically ill patients with acute myocardial infarction (AMI), especially in large data studies, which we aimed to do in this study.MethodsFor this retrospective study, we extracted analysis data from a famous online intensive care unit database, the Medical Information Mart for Intensive Care (MIMIC)-IV. Included patients were divided into four groups according to the serum lactate level on admission. Hospital mortality and mortality over time were the main outcomes. To explore the relationship between admission hyperlactatemia and outcomes in critically ill patients with AMI, logistic regression, Cox regression, Kaplan-Meier curves, and subgroup analyses were used.Results2171 patients matching the selection criteria were enrolled in this study. After adjusting for potential confounding factors, hyperlactatemia on admission contributed to increased short-term mortality in critically ill patients with AMI. The adjusted odds ratio for hospital mortality were 1.62, 3.46 and 5.28 in the mild, moderate, and severe hyperlactatemia groups (95% CI: 1.20-2.18, 2.15-5.58, and 2.20-12.70, respectively). The adjusted hazard ratio for 7-day and 30-day mortality were 1.99 and 1.35 (95% CI: 1.45-2.73 and 1.09-1.67) in the mild hyperlactatemia group, 3.33 and 2.31 (95% CI: 2.22-4.99 and 1.72-3.10) in the moderate hyperlactatemia group, 4.81 and 2.91 (95% CI: 2.86-8.08 and 1.88-4.50) in the severe hyperlactatemia group. The adjusted hazard ratio for 1-year and 5-year mortality were 2.03 and 1.93 (95% CI: 1.58-2.62 and 1.52-2.47) in the moderate hyperlactatemia group, 1.92 and 1.74 (95% CI: 1.28-2.89 and 1.17-2.59) in the severe hyperlactatemia group. Subgroup analyses indicated that the positive correlation between serum lactate level on admission and short-term mortality of critically ill patients with AMI was similar in the subgroups of cardiogenic shock and acute heart failure (P for interaction > 0.05).ConclusionHyperlactatemia, especially moderate and severe hyperlactatemia, on admission is closely related to higher short-term mortality incidence in critically ill patients with AMI. The relationship between serum lactate level on admission and short-term mortality of critical AMI patients is stable in subgroups of cardiogenic shock and acute heart failure.

Project description:BackgroundPlatelets have been involved in both immune surveillance and host defense against severe infection. To date, whether platelet phenotype or other hemostasis components could be associated with predisposition to sepsis in critical illness remains unknown. The aim of this work was to identify platelet markers that could predict sepsis occurrence in critically ill injured patients.MethodsThis single-center, prospective, observational, 7-month study was based on a cohort of 99 non-infected adult patients admitted to ICUs for elective cardiac surgery, trauma, acute brain injury, and post-operative prolonged ventilation and followed up during ICU stay. Clinical characteristics and severity score (SOFA) were recorded on admission. Platelet activation markers, including fibrinogen binding to platelets, platelet membrane P-selectin expression, plasma soluble CD40L, and platelet-leukocytes aggregates were assayed by flow cytometry at admission and 48 h later, and then at the time of sepsis diagnosis (Sepsis-3 criteria) and 7 days later for sepsis patients. Hospitalization data and outcomes were also recorded.MethodsOf the 99 patients, 19 developed sepsis after a median time of 5 days. These patients had a higher SOFA score at admission; levels of fibrinogen binding to platelets (platelet-Fg) and of D-dimers were also significantly increased compared to the other patients. Levels 48 h after ICU admission no longer differed between the two patient groups. Platelet-Fg % was an independent predictor of sepsis (P = 0.0031). By ROC curve analysis, cutoff point for Platelet-Fg (AUC = 0.75) was 50%. In patients with a SOFA cutoff of 8, the risk of sepsis reached 87% when Platelet-Fg levels were above 50%. Patients with sepsis had longer ICU and hospital stays and higher death rate.ConclusionsPlatelet-bound fibrinogen levels assayed by flow cytometry within 24 h of ICU admission help identifying critically ill patients at risk of developing sepsis.

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Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients.

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Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients.

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